-
1 Comment
Guangzhou Zhiguang Electric Co., Ltd is currently in a long term uptrend where the price is trading 42.7% above its 200 day moving average.
From a valuation standpoint, the stock is 70.1% cheaper than other stocks from the Technology sector with a price to sales ratio of 2.4.
Guangzhou Zhiguang Electric Co., Ltd's total revenue rose by 11.4% to $587M since the same quarter in the previous year.
Its net income has increased by 269.4% to $7M since the same quarter in the previous year.
Finally, its free cash flow grew by 5.6% to $59M since the same quarter in the previous year.
Based on the above factors, Guangzhou Zhiguang Electric Co., Ltd gets an overall score of 5/5.
ISIN | CNE1000006T7 |
---|---|
Sector | Technology |
Industry | Electronic Components |
Exchange | SHE |
CurrencyCode | CNY |
Market Cap | 4B |
---|---|
PE Ratio | None |
Target Price | 12.5 |
Beta | 0.3 |
Dividend Yield | 1.7% |
Guangzhou Zhiguang Electric Co.,Ltd. provides digital energy technology products and services worldwide. The company offers energy digital control systems, enterprise energy management systems, energy storage EMS, energy storage smart cloud networks, optical storage charging micro-grid smart energy management platforms, intelligent power operation and maintenance systems, and photovoltaic power plant operation management systems. It also provides digital energy technology and products, such as digital energy equipment and smart cables; and industrial and strategic investment products and services. The company was founded in 1999 and is headquartered in Guangzhou, China.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 002169.SHE using our backtest tool. PyInvesting provides the backtesting software for you to backtest your investment strategy. Our backtest software is written using Python code and allows you to backtest stock, backtest etf, backtest options, backtest crypto and backtest forex online. Our backtesting Python framework is highly robust and gives you a realistic simulation of how your strategy would have performed in the past using backtest data.
© PyInvesting 2025